Cross-modality synthesis of ultra-widefield fluorescein angiography from ultra-widefield color fundus photography for diabetic retinopathy via UWFDR-GAN.

backgroundWhile ultra-widefield fluorescein angiography (UWF-FA) is essential for evaluating retinal vascular pathology in diabetic retinopathy (DR), its invasive nature limits its clinical application. This study aimed to develop and evaluate UWFDR-GAN, a generative adversarial network (GAN) framework for translating ultra-widefield color fundus photography (UWF-CFP) into UWF-FA specifically for DR patients.

methodsA total of 270 paired UWF-CFP and UWF-FA images were collected from patients with DR, comprising 73 pairs of mild non-proliferative diabetic retinopathy (NPDR), 47 pairs of moderate NPDR, 82 pairs of severe NPDR, and 68 pairs of proliferative diabetic retinopathy (PDR). We first employed a self-supervised keypoint detection framework for precise cross-modal image registration. The generation network incorporated discrete wavelet transform/inverse transform (DWT/IDWT) to preserve high-frequency details and a Swin Transformer-based multi-scale discriminator to enhance structural realism. We quantitatively compared the performance of our model against several state-of-the-art methods, including pix2pix, pix2pixHD, and UWAFA-GAN, using objective evaluation metrics: the Multi-Scale Structural Similarity Index Measure (MS-SSIM), Peak Signal-to-Noise Ratio (PSNR), Fréchet Inception Distance (FID), and Inception Score (IS).

resultsUWFDR-GAN achieved the best quantitative performance (MS-SSIM: 0.7214; PSNR: 20.00; FID: 77.48; IS: 1.0123), outperforming all comparison models. Qualitatively, it preserved global vascular architecture and demonstrated superior reconstruction of DR-specific lesions, particularly neovascularization and non-perfusion areas.

conclusionsUWFDR-GAN provided a non-invasive ultra-widefield vascular assessment solution for clinical DR management, demonstrating potential to reduce reliance on invasive fluorescein imaging.